Wealthfront Classic Portfolio Investment Methodology White Paper

Typically, you don’t get to “take home” pre-tax returns, as taxes may reduce the amount of return you ultimately keep. Therefore, to determine an appropriate asset allocation for taxable and tax-deferred (retirement) accounts, we need to determine each asset class’s net-of-fee, after-tax return. The taxation of investment returns depends on their composition (income vs. capital gains), and the type of account they are held in (taxable vs. retirement). In a taxable account, income distributions (dividends and interest) are generally subject to taxation at ordinary income rates, and are taxed when they are distributed. There are a few important exceptions to this. First, some dividends, known as “qualified dividends,” may be taxed at lower, long-term capital gains rates. Second, interest on municipal bonds is exempt from taxation at the federal level, and potentially the state level, (e.g., it is possible to be state-tax exempt if the bonds are issued by the state you are a resident of). Unlike in a taxable account, where only the net-of-tax portion of the distribution can accumulate over time, in a retirement account, income distributions are not taxed at the time they happen, which allows your money to accumulate in a tax-deferred fashion.

Finally, the two account types differ in how capital gains and withdrawals are taxed. In a taxable account, even in the absence of add-on deposits, the cost basis of your investments increases over time as the net-of-tax amount of the income distribution is reinvested. When you withdraw your assets, any gain relative to this cost basis is taxed at long-term capital gains rates (assuming you have held the investment for at least a year). In a traditional retirement account, you pay income tax on the entire withdrawal amount — contributions plus appreciation — at ordinary income rates, since the investments were made with pre-tax dollars. In a Roth retirement account, no taxes are due on withdrawals (assuming you’ve met the holding and age requirements), since the investment was made with after-tax dollars (i.e. you already paid income tax on the amount invested). The main difference between traditional and Roth accounts is that traditional accounts allow for tax deferral, while Roth accounts offer tax-free growth and withdrawals, provided certain conditions are met.

To estimate how much of the pre-tax expected return is likely to be lost to taxes (i.e. tax drag) on an annualized basis, we need to determine:

1. For each asset class, the fraction of the expected return that will be distributed each year, either in the form of ordinary income (dividends, interest) or capital gains.
2. For each asset class, the fraction of dividend distributions that will be treated as qualified, and thus subject to taxation at long-term capital gains rates.
3. For each account type, the projected time until liquidation, which is necessary to amortize the taxes due at liquidation (capital gains in taxable accounts, and ordinary income in traditional retirement accounts) over the life of the investment.

For each asset class, we estimate the fraction of the total return that will be distributed each year based on historical dividend yields. Given the low index turnover and the tax efficient nature of ETFs, we assume capital gain distributions are zero, which is consistent with historical data. The fraction of distributions subject to qualified dividend treatment is also estimated based on historical data. For illustrative purposes, in Table 3, we assume a combined ordinary income tax rate of 28% (24% federal + 4% state), applicable to Wealthfront’s median client weighted by assets. We further assume the household is subject to a 15% federal long-term capital gains rate and a 24% federal short-term capital gains rate. (Note that for our taxable Automated Investing Account, we optimize our tax assumptions based on information such as your tax filing status, annual household income, and state of residence. We will explain this in more detail in the Personalization with Tax Rates section later). Finally, we assume that investments in the taxable account will be liquidated in 10 years, whereas those in the retirement account will be liquidated in 30 years. The majority of Wealthfront’s clients are under 45 years of age, and have a relatively long horizon until they begin drawing on their retirement accounts. We use a shorter horizon for taxable accounts as clients may use those assets for nearer-term goals, such as a home purchase or educational expenses. The applicable tax rates and the household’s income tax bracket are assumed to remain unchanged over this period.

We simulate the pre-tax returns of each asset class, apply the relevant tax rules within the two account types, and then compute the net-of-fee, after-tax return. This methodology allows us to assess the combined impact of taxes on the intermediate distributions, as well as the liquidation of the account. The difference between the annualized pre-tax and after-tax rates captures the tax drag, i.e. the amount lost to taxes annually. Table 3 reports the tax drag and net-of-fee, after-tax rates of return for each asset class when held in taxable and retirement accounts.

We use these net-of-fee, after-tax rates of return as inputs to the mean-variance optimization to determine the efficient frontier. The estimates change over time as we incorporate new market data into our models, and we periodically release new asset allocations based on our latest data and portfolio construction methodology. Because the adoption of a new allocation may result in tax consequences from realized gains, we will not transition you to a new allocation without offering an opportunity to choose to stay on your existing one.

It is important to note that we did not consider the benefits from Tax-Loss Harvesting when assessing expected returns for taxable accounts. Asset classes differ in their volatilities and thus their tax-loss harvesting potential, which would change their after-tax expected returns.

Variance-Covariance Matrix

To derive an estimate of the asset class covariance matrix, we rely on historical data, combined with factor analysis using the Wealthfront Factor Model, the same factor model used for expected return assumptions. We first estimate how each asset class is “exposed” to the factors using long-term regressions with daily return data, and the covariances between the factors. This captures any common sources of variation across asset classes. In addition, we also estimate any asset class-specific variations with the residual returns from the regressions above. The final covariance matrix is a combination of estimated variations from common sources and asset class-specific variations. We believe the factor model approach provides a robust estimation of asset class covariance and makes it less susceptible to influences of historical events with large returns that are unlikely to repeat in the future.

Since covariances are the product of individual asset volatilities and the correlation between the two assets, for easier comprehension, we present the two components of the asset covariance matrix separately. Table 4 shows the estimated volatility of the individual asset classes, and Table 5 shows the estimated correlation matrix of asset class returns. 

The volatility estimates confirm that stocks are generally riskier than bonds, foreign stocks are generally riskier than US stocks, and that, even within an asset class there can be considerable variation in risk (e.g. US bonds vs. US corporate bonds vs. emerging market bonds). Finally, investments focused on a smaller subset of assets (e.g. real estate) tend to be less diversified and have higher volatility.

The correlations between US stocks and US bonds are close to zero, showing that bonds have been a very good diversifier for equity investments. Correlations between different types of stocks have increased recently reflecting greater global integration across economies and capital markets. Similarly, real estate is more correlated with broad equity indices today than in the 1980s and 1990s. Across different types of bonds, correlations with equities range from zero (US bonds), to slightly positive (US corporate bonds), and very positive (emerging market bonds). This trend reflects the increasing credit risk of these different types of bonds.

Portfolio Construction

We use the estimates from the variance-covariance matrix of asset class returns, and the net-of-fee, after-tax expected returns for each asset class as inputs to the mean-variance optimization to determine the optimal portfolio for each level of risk. Additionally, we enforce minimum and maximum allocation constraints for each asset class that are displayed in Table 6. The minimum allocation constraints are set at zero in order to ensure that the optimized portfolios are long-only (i.e. do not involve any short positions).  We selected 35% as the maximum allocation for most asset classes to help ensure sufficient diversification. Other respected sources (including Swensen, 2005) recommend similar maximum asset class allocations. US stocks are an exception, with a maximum allocation at 45%. US stocks represent a significant proportion of the world’s stocks and make up roughly 64% of the MSCI All-Country World Index (ACWI)* as of May 2024. We exclude REITs from taxable portfolios, as tax forms distributed by REIT ETFs are commonly restated or distributed late, complicating tax filings for investors.

* MSCI ACWI FactSheet (May 2024)

Personalization with Tax Rates

For taxable accounts, Wealthfront optimizes the asset allocation recommendation based on your federal and state tax rates, in an effort to maximize your after-tax expected return while maintaining an appropriate risk level. 

The personalization considers differences in tax implications for different asset classes. As mentioned in the Expected Returns section above, interest from municipal bonds is typically exempt from federal tax. It is also exempt from state tax if the municipal bonds are issued from your state of residence. Interest from treasury bonds, including TIPS, is typically exempt from state tax. All else being equal, bonds with more tax exemptions tend to have lower pre-tax returns, and are therefore more appealing to investors with higher marginal tax rates. We also consider tax rate differences for gains from different sources in our after-tax expected return calculation, which informs the objective of our mean-variance optimization: Income such as dividends and interest, as well as short-term capital gains are taxed at ordinary income rates, whereas “qualified dividends” and long-term capital gains are taxed at the lower long-term capital gain rates. For most states, we use a “national” municipal bond ETF holding bonds from states across the country. For states with high tax rates, we will consider state-specific municipal bond ETFs if they meet our liquidity and cost criteria and may offer the potential to improve after-tax, after-fee expected returns. Currently, California is the only state which meets these criteria. We’ll discuss our approach to creating California-specific allocations in the next section.

There are seven federal marginal income tax brackets, ranging from 10% to 37%. State income tax can vary widely across states. Several states such as Florida, Washington, and Texas do not have a state income tax at all. Other states have maximum rates of 8% or higher, with California topping the list with rates up to 13.3%. As our portfolio optimization’s objective is to maximize after-tax expected return of the portfolio, different tax brackets will impact the objective function and potentially result in different allocations. However, we find that the optimal portfolio allocations are insensitive to the exact choices of tax rates. In fact, our research demonstrates that three representative portfolios—each optimized for a specific combination of state and federal taxes—are generally sufficient to deliver an expected after-tax return within 0.02% of the optimal return for any combination of tax rates. 

Table 7 shows the assumed state and federal tax rates used in the three representative portfolios. The medium tax level portfolio’s 24% federal income tax rate, 15% long-term capital gain tax rate, and 4% state tax rate are approximately the median tax rates of our clients. 

For the high tax level portfolio, we chose 35%, the median of the three highest federal tax rates of 32%, 35% and 37%. For long-term capital gain tax rates, there are a total of three rates: 0%, 15%, and 20%. We picked 20% for high tax level portfolios. For state tax, the number varies across states, with California’s 13.3% as the maximum. We chose 8%, which is slightly lower than the midpoint between 4% and 13.3%, as only two states, New York and California, have state tax rates above 10%. In the next section, we will recommend a specific allocation for California residents, which leaves New York as the only state with rates above 10% for the non-California allocations. For the low tax level portfolio, we chose the median of the three lowest federal income tax rates of 10%, 12%, and 22%. For the long-term capital gains tax rate, we choose the only option below 15%, which is 0%. For state tax, we also choose 0% to be sufficiently different from 4% and to better represent states where there is no state income tax. 

In order to find the most tax-efficient portfolio for you, we first infer your marginal federal and state income and long-term capital gain tax rates based on your responses to the following three questions: your tax filing status, annual household income, and state of residence. We use marginal tax brackets because any realized earnings from the Automated Investing Account will be in addition to the annual income you already earn. We also assume a standard deduction from the annual income when inferring the tax brackets. We then calculate the client’s expected after-tax return for each of the three representative portfolios, and assign you to the portfolio that aims to deliver the best expected return. Table 8 shows the assigned portfolio for every possible combination of state (rounded to the nearest percent) and federal income tax rates based on this methodology. Long-term capital gains tax is not included in the mapping because in most cases the income range for a specific federal income rate maps uniquely to one long-term capital gain tax rate. In the few cases where there are two possible long-term capital gain tax rates, their optimal portfolio choices are the same. It turns out that state tax rates actually don’t matter in assigning the optimal portfolio for non-California investors. This is because the main tax benefit comes from national municipal bonds, which only have a federal tax exemption. State tax rates matter more for California residents (as we will see in the next section), as California municipal bonds are exempt from both federal and state income taxes.

Allocations for California Residents

We offer special allocations for residents of California. California is unique among states because:

1. It has very high tax rates — its top marginal rate of 13.3% is the highest in the country.
2. There is a liquid and low-cost ETF (for example, we use the iShares California Muni Bond ETF, with an expense ratio of 0.08%) dedicated to municipal bonds issued in California.

Remember that interest from municipal bonds issued in an investor’s state of residence is exempt from state taxes as well as federal taxes. This exemption, along with the availability of the CA-specific municipal bond ETF allows us to create portfolios that strive for higher after-tax, after-fee expected returns for California residents – especially those in the highest tax brackets.

We take a holistic approach to our California customization. Instead of simply replacing the national municipal bond weight in the non-California allocations, we use the same mean-variance optimization approach in an attempt to arrive at the optimal portfolios. This way, we are able to take into account California municipal bonds’ specific after-tax, after-fee expected return, as well as their volatility and correlations with other asset classes.

For California residents, our research demonstrates that three representative portfolios may be sufficient to deliver an expected after-tax return within 0.05% of the optimal return for any combination of tax rates. In Table 9, we show the assumed tax rates for these representative portfolios, which reflect California state tax rates. The tax rates are higher than non-California ones due to California’s higher state tax rates.

We use the methodology described previously in an effort to find the optimal portfolio for each possible state and federal tax rate combination. Long-term capital gains rates are also not included here because they do not impact the portfolio choices.

Taxable and Retirement Account Allocations

We construct seven sets of portfolio allocations: three for taxable non-California accounts (low, medium, and high tax levels), three for taxable California accounts and one for retirement accounts. Each set of portfolio allocations contains twenty portfolios with varying levels of portfolio volatility. We define the lowest volatility allocation to have a Risk Score of 0.5 and the highest a Risk Score of 10.

Figure 1 presents the optimal allocations for non-California taxable accounts. Depending on the targeted level of risk and assumed tax rates, the portfolios contain between five and seven asset classes including US stocks, foreign developed stocks, emerging market stocks, dividend growth Stocks, US bonds, US corporate Bonds, municipal bonds, and TIPS. As tax rates increase, the allocations include more municipal bonds because they generally have higher net-of-fee, after-tax expected return due to their federal tax exemption. As the risk level increases from left to right, the allocation to lower risk/lower return asset classes such as TIPS and municipal bonds decreases, while the allocation to higher risk/higher return asset classes such as US stocks, foreign developed stocks, and emerging market stocks increases. Equities not only offer higher potential returns, but are more tax efficient, since often a sizable portion of their dividends are taxed at qualified dividend rates, which are less than the ordinary income tax rates that are applied to bond interest.

Figure 2 presents the optimal allocations for California taxable accounts. Similar to non-California allocations, depending on the targeted level of risk and assumed tax rates, the California portfolios contain between five and seven of roughly the same asset classes, with the only difference being California municipal bonds are used instead of national municipal bonds. As tax rates increase, the allocations include more California municipal bonds. For the rest of the asset classes, weight trends look fairly similar to non-California allocations, with small differences in actual weights in an effort to best match the target risk level and maximize after-tax expected returns. The California medium tax allocations are closer to the California high tax allocation than non-California’s medium tax allocations are to their high tax counterparts. This is because California’s medium tax representative portfolio has a much higher state tax rate at 9.3%, compared to the 4% state tax rate for non-California portfolios. As a result, it is more similar to the non-California high tax allocations.

Figure 3 presents the optimal asset allocations for retirement accounts. Because retirement accounts, like an IRA, are tax-deferred, tax rate personalization does not offer meaningful benefits to these accounts, so we offer only one set of allocations by varying target risk levels. The allocations include eight unique asset classes, with five to eight applied to any one portfolio. As the risk level increases from left to right, allocation to conservative asset classes such as US bonds, TIPS and corporate bonds decreases, while allocation to more aggressive asset classes such as US stocks, foreign developed stocks, emerging market stocks, and real estate increases. Emerging market bonds behave somewhere between conservative and aggressive asset classes. 

Handling Small Accounts

Wealthfront automated investing accounts can start as small as $500, which doesn’t always provide sufficient cash for meaningful exposure to all of the asset classes we recommend. As a result, for such small accounts, we use a process of holistic optimization to select the available investment ETFs that best match the expected performance of the desired portfolio allocation while attempting to minimize the “cash drag” from any uninvested assets. Our backtesting shows that this optimization process typically results in portfolios with a smaller number of asset classes, minimal cash drag, and a strong match for the historical performance of the desired target portfolio. What’s more, as these accounts potentially grow in size they generally evolve into our typical portfolio allocations.

Determining Risk Tolerance

Once the Efficient Frontier has been established, it is necessary to pinpoint your risk tolerance in order to identify the ideal asset allocation for your needs. Rather than asking the typical 25 questions asked by many financial advisors to identify an individual’s risk tolerance, Wealthfront combed behavioral economics research to simplify our risk identification process to only a few questions. For example, we are able to project your income growth and saving rate based on your age and current income. We ask you questions to evaluate both your objective capacity to take risk and subjective willingness to take risk. Our view is that sophisticated algorithms can do a better job of evaluating risk than the average traditional advisor.

We ask subjective risk questions to determine both the level of risk you are willing to take and the consistency among your answers. The less consistent the answers, the exponentially less risk-tolerant you are likely to be. For example, if you are willing to take a lot of risk in one case and very little in another, then you are inconsistent and are therefore assigned a lower Risk Score than the simple weighted average of your answers.

We ask objective risk questions to estimate with as few questions as possible whether you are likely to have enough money saved at retirement to afford your likely spending needs. The greater the excess income, the more risk you are able to take. Conversely, if your expected retirement income is less than your likely retirement spending needs, then you cannot afford to take much risk with your investments.

Our overall Risk Score combines subjective and objective risk tolerance, with a heavier weighting to whichever component is more risk averse. We chose this approach because behavioral economics research shows individuals consistently overstate their true risk tolerance, especially male investors who are educated and overconfident (Barber & Odean, 2001). Relying on an investor’s biased answers may lead to a more volatile portfolio than appropriate, which could increase the likelihood the investor sells when the market declines. DALBAR published a study that observed the average equity mutual fund investor underperformed the S&P 500 by 2.14% on an annualized basis during the 30-year period 1994-2023 due to consistently buying after the market has risen and selling when the market declines (DALBAR, 2024).

The composite Risk Scores range from 0.5 (most risk averse) to 10.0 (most risk tolerant) in 0.5 increments. In turn, each Risk Score corresponds to one of the twenty asset allocations described in the previous section.

We email you periodically to determine if anything in your financial profile has changed that may affect your risk tolerance. For example, getting married, having kids, benefiting from equity appreciation associated with an IPO, or being promoted to a significantly higher paying job can have a major impact on the Risk Score we apply and therefore your ideal investment mix. 

We inform our customers about the impacts of changing their Risk Score frequently, and that it might not be appropriate for their ultimate goals. This is because we believe attempting to time the market is one of the most serious mistakes investors can make, and changing Risk Scores frequently should not be used as a tool to try to time the market. We recommend that you review their Risk Score annually and only consider updating it every three years or so, or if you experience a significant change in financial circumstances.

Rebalancing and Ongoing Monitoring

The composition of any investment portfolio will naturally drift as capital markets move and certain holdings outperform others. This typically results in two adverse outcomes in our experience: (1) portfolio risk increases as higher-risk portions of the portfolio grow beyond their original allocations, and (2) allocations become sub-optimally mixed. To maintain the intended risk level and asset allocations, a portfolio must be periodically rebalanced. Sophisticated algorithms are required to optimize rebalancing subject to tax and trading expense effects.

Wealthfront monitors our clients’ portfolios and periodically rebalances each portfolio when dividends from ETFs accrue, a deposit or withdrawal has been made, or if movements in their relative allocations justify a change. Our rebalancing algorithms trade off deviations from the target portfolio with the tax consequences of selling appreciated assets. We use cash inflows to buy underweight asset classes and threshold-based rebalancing instead of time-based rebalancing in an effort to reduce turnover, taxes, and trading costs. Rebalancing will usually reduce risk over time, but not necessarily increase returns.

It is important to note that your asset allocation will typically need to be adjusted over time as your investment goals and risk tolerance may change. Wealthfront recommends you review your investment plans in detail every three to five years to determine whether your risk tolerance and target allocation should be updated. We also remind you on a quarterly basis to keep us informed of any such changes.

Conclusion

Wealthfront combines the judgment of its investment team with state of the art optimization tools to identify efficient portfolios. We strive to deliver the maximum net-of-fee, after-tax, real investment return for each client’s particular tolerance for risk. This means we will continue to look for meaningful ways to improve our investment methodology in the future while continuously monitoring and periodically rebalancing our clients’ portfolios to maximize returns while maintaining their calculated risk tolerance. We believe following this process will lead to outstanding long-term financial outcomes for our clients.

Bibliography

• Barber, B., & Odean, T. (2001). Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment. Quarterly Journal of Economics.
• Black, F., & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal.
• Bogle, J. (2009). Common Sense on Mutual Funds. Wiley.
• Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). Determinants of Portfolio Performance. Financial Analyst Journal, 39-44.
• Brinson, G. P., Singer, B. D., & Beebower, G. L. (1991). Determinants of Portfolio Performance II: An Update. Financial Analyst Journal, 40-48.
• DALBAR. (2016). Quantitative Analysis of Investor Behavior. DALBAR.
• Fama, Eugene F.; French, Kenneth R. (1992). The Cross Section of Expected Stock Returns, Journal of Financial, 427–465.
• Fama, Eugene F.; French, Kenneth R. (1993). Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 3–56.
• Ibbotson, R.G. & Kaplan, P.D. (2000). Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance? Financial Analyst Journal, 26-33.
• Ledoit, O. and M. Wold, Honey, I Shrunk the Sample Covariance Matrix, Journal of Portfolio Management.
• Markowitz, H. (1952). Portfolio Selection. Journal of Finance.
• Malkiel, B. (2012). A Random Walk Down Wall Street. W. W. Norton & Company.
• Malkiel, B. & Ellis, C. (2013). The Elements of Investing. John Wiley & Sons.
• Morningstar (2020), US Fund Fee Study
• Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risks. Journal of Finance.
• Swensen, D. (2005). Unconventional Success. Free Press.

Disclosure

This Wealthfront Investment Methodology White Paper has been prepared by Wealthfront Advisers LLC (“Wealthfront”) solely for informational purposes only.  Nothing contained herein should be construed as (i) an offer to sell or solicitation of an offer to buy any security or (ii) any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The information set forth herein has been obtained or derived from sources believed by Wealthfront to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. The information set forth herein has been provided to you as secondary information and should not be the primary source for any investment or allocation decision. This document is subject to further review and revision.

To capture the historical performance of asset classes, we used historical return data for instruments tracking the following indices: US Stocks (CRSP US Total Market Index), Foreign Developed Stocks (MSCI EAFE Index), Emerging Market Stocks (MSCI Emerging Markets Index), Dividend Stocks (Dow Jones US Dividend 100 Index), US Govt Bonds (Barclays US Aggregate Bond Index), US Corporate Bonds (iBoxx Liquid Investment Grade Index), Emerging Market Bonds (JPMorgan EMBI Global Core Index), Municipal Bonds (S&P Municipal Bond Index), TIPS (Barclays US Inflation-linked Bond Index), Real Estate (FTSE NAREIT US Real Estate Index). The choices made by Wealthfront to use certain instruments  may affect the performance calculations, and different choices would result in different performance estimates. Various strategies and assumptions may affect performance, such as ETF selection, ETF tracking error and expenses, and rebalancing of allocations.

To construct forward-looking projections of each asset class’s expected returns, we combined forecasts from the Capital Asset Pricing Model (CAPM) with forecasts from a proprietary multi-factor model (“Views”) using the Black-Litterman framework. The CAPM forecast is constructed on the basis of: (a) an estimate of the composition of the global market portfolio; (b) an estimate of the variance-covariance matrix of asset class returns estimated from monthly historical data; and, (c) an assumed parameter measuring the risk tolerance of an average investor. To construct views we combine estimates of each asset class’s exposure to a collection of economic risk factors (obtained using historical return data) with projections of the forward looking risk-free rates and risk premia (obtained via Monte Carlo simulation of the Wealthfront Factor Model).

The projections and other information generated by the Wealthfront Factor Model (WFM) are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. WFM results will vary with each use and over time. The WFM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the WFM. More importantly, the WFM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based.

The WFM is a proprietary financial simulation tool developed and maintained by Wealthfront’s Research group. The model forecasts distributions of future realization of economic risk factors, valuation ratios, and US Treasury yields. The theoretical and empirical foundation for the WFM is that the returns of various asset classes reflect the compensation investors require for the passage of time (risk-free rate) and for bearing different types of systematic risk (beta). At the core of the model are estimates of the dynamic statistical relationship between risk factors and asset returns, obtained from statistical analysis based on available monthly financial and economic data. Using a system of estimated equations, the model then applies a Monte Carlo simulation method to construct forward-looking forecasts. The model generates a large set of simulated outcomes for each asset class over several time horizons. Forecasts are obtained by computing measures of central tendency in these simulations. Results produced by the tool will vary with each use and over time.

The information in this document may contain projections or other forward-looking statements regarding future events, targets, forecasts or expectations that are based on Wealthfront’s current views and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. Neither the author nor Wealthfront or its affiliates assumes any duty to, nor undertakes to update forward looking statements.  Actual results, performance or events may differ materially from those in such statements due to, without limitation, (1) general economic conditions, (2) performance of financial markets, (3) changes in laws and regulations and (4) changes in the policies of governments and/or regulatory authorities. Any opinions expressed herein reflect our judgment as of the date hereof and neither the author nor Wealthfront undertakes to advise you of any changes in the views expressed herein.

Hypothetical expected returns information have many inherent limitations, some of which, but not all, are described herein. No representation is being made that any client account will or is likely to achieve performance returns or losses similar to those shown herein. In fact, there are frequently sharp differences between hypothetical expected returns and the actual returns subsequently realized by any particular trading program. One of the limitations of hypothetical expected returns is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or adhere to a particular trading program in spite of trading losses are material points which can adversely affect actual trading results.  The hypothetical expected returns contained herein represent the application of the rule-based models as currently in effect on the date first written above and there can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect actual trading results. Hypothetical expected returns are presented for illustrative purposes only. No representation or warranty is made as to the reasonableness of the assumptions made or that all assumptions used in achieving the returns have been stated or fully considered. Changes in the assumptions may have a material impact on the hypothetical returns presented.

Correlation is a measure of statistical association, or dependence, between two random variables. The values presented here are based on a particular historical sample period, data frequency, and are specific to the assets/indices used in the analysis. Correlations may change over time, such that future values of correlation may significantly depart from those observed historically.

Past performance is no guarantee of future results, and any hypothetical returns, expected returns, or probability projections may not reflect actual future performance. Actual investors may experience different results from the expected or hypothetical returns shown. There is a potential for loss that is not reflected in the hypothetical information portrayed. The expected returns shown do not represent the results of actual trading using client assets but were achieved by means of the retroactive application of a model designed with the benefit of hindsight.

No representation or warranty, express or implied, is made or given by or on behalf of the author, Wealthfront or its affiliates as to the accuracy and completeness or fairness of the information contained in this document, and no responsibility or liability is accepted for any such information.  By accepting this document in its entirety, the recipient acknowledges its understanding and acceptance of the foregoing statement.

Wealthfront Advisers and its affiliates do not provide legal or tax advice and do not assume any liability for the tax consequences of any client transaction. Clients should consult with their personal tax advisors regarding the tax consequences of investing with Wealthfront Advisers and engaging in these tax strategies, based on their particular circumstances. Clients and their personal tax advisors are responsible for how the transactions conducted in an account are reported to the IRS or any other taxing authority on the investor’s personal tax returns. Wealthfront Advisers assumes no responsibility for the tax consequences to any investor of any transaction.

The possibility of tax advantages from state municipal bond ETFs is dependent on a client’s state of residence and individual tax situation. Clients should consult with their personal tax advisors regarding the tax consequences of investing with Wealthfront Advisers and engaging in these tax strategies, based on their particular circumstances. Clients and their personal tax advisors are responsible for how the transactions conducted in an account are reported to the IRS or any other taxing authority on the investor’s personal tax returns. Wealthfront Advisers assumes no responsibility for the tax consequences to any investor of any transaction.

Any links provided to other server sites are offered as a matter of convenience and are not intended to imply that Wealthfront Advisers or its affiliates endorses, sponsors, promotes and/or is affiliated with the owners of or participants in those sites, or endorses any information contained on those sites, unless expressly stated otherwise.

All investing involves risk, including the possible loss of money you invest, and past performance does not guarantee future performance. Please see our Full Disclosure for important details.

Investment management and advisory services are provided by Wealthfront Advisers LLC (“Wealthfront Advisers”), an SEC-registered investment adviser, and brokerage related products, including the Cash Account, are provided by Wealthfront Brokerage LLC, a Member of  FINRA/SIPC.

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